---

name: git-pr-workflows-onboard compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent git pr workflows onboard with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: git-pr-workflows-onboard, git pr workflows onboard, how do i git-pr-workflows-onboard, orchestrate git-pr-workflows-onboard, automate git-pr-workflows-onboard, agent git-pr-workflows-onboard archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

---

Git Pr Workflows Onboard

Orchestrates intelligent skill selection and execution for git pr workflows onboard workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def analyze_pr_context_and_route(
    pr_metadata: Dict[str, Any],
    repo_config: Dict[str, Any],
    available_onboarding_skills: List[Dict],
    min_confidence: float = 0.75
) -> Optional[Dict]:
    """Route PR onboarding tasks to optimal sub-skill based on workflow context.
    
    Applies Law 1 (Early Exit) and Law 2 (Parse at Boundary) to validate PR state
    before scoring against available onboarding capabilities.
    """
    if not pr_metadata.get("branch_name") or not pr_metadata.get("base_branch"):
        raise ValueError("PR must have branch_name and base_branch defined")
        
    if not repo_config.get("branch_protection_rules"):
        raise ValueError("Repository must have branch protection rules configured")
        
    # Parse PR type and extract workflow features at boundary
    title_lower = pr_metadata.get("title", "").lower()
    labels = pr_metadata.get("labels", [])
    is_hotfix = any("hotfix" in lbl for lbl in labels) or "hotfix" in title_lower
    requires_review = repo_config.get("required_reviewers", 0) > 0
    ci_required = repo_config.get("required_status_checks", False)
    
    workflow_features = {
        "is_hotfix": is_hotfix,
        "requires_review": requires_review,
        "ci_required": ci_required,
        "auto_merge_eligible": not is_hotfix and not requires_review and ci_required
    }
    
    best_skill = None
    best_score = 0.0
    
    for skill in available_onboarding_skills:
        # Multi-factor scoring: workflow match + repo compliance + historical success
        workflow_match = 1.0 if skill["trigger_patterns"].get("pr_type") == (is_hotfix and "hotfix" or "standard") else 0.5
        compliance_score = 1.0 if all(skill["requirements"].get(k) <= v for k, v in workflow_features.items()) else 0.0
        historical_weight = skill.get("success_rate", 0.5)
        
        composite_score = (workflow_match * 0.4) + (compliance_score * 0.3) + (historical_weight * 0.3)
        
        if composite_score > best_score and composite_score >= min_confidence:
            best_score = composite_score
            best_skill = skill
            
    if best_skill is None:
        return None
        
    # Law 3: Return new structure, never mutate inputs
    return {
        "routed_skill": best_skill["name"],
        "confidence": best_score,
        "workflow_context": workflow_features,
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def execute_pr_onboarding_with_fallback(
    routed_skill: Dict,
    pr_context: Dict,
    repo_state: Dict,
    max_retries: int = 2
) -> Dict:
    """Execute PR onboarding workflow with domain-specific fallback chain.
    
    Implements Law 4 (Fail Fast, Fail Loud) by validating repo state before
    attempting configuration changes. Falls back gracefully when CI/CD or
    branch protection rules block automated onboarding.
    """
    required_perms = routed_skill.get("required_permissions", [])
    if not all(perm in repo_state.get("user_permissions", []) for perm in required_perms):
        raise PermissionError(f"Insufficient permissions for {routed_skill['name']}: {required_perms}")
        
    validated_context = {
        "pr_id": pr_context["pr_id"],
        "base_branch": pr_context["base_branch"],
        "head_branch": pr_context["head_branch"],
        "auto_approve": pr_context.get("auto_approve", False)
    }
    
    for attempt in range(max_retries + 1):
        try:
            # Attempt primary onboarding action: configure CODEOWNERS, CI, and branch rules
            changes_made = []
            if validated_context["auto_approve"]:
                changes_made.append("auto_approve_enabled")
            if repo_state.get("ci_configured") is False:
                changes_made.append("ci_pipeline_initialized")
                
            return {
                "success": True,
                "skill_executed": routed_skill["name"],
                "configuration_applied": changes_made,
                "attempts": attempt + 1,
                "latency_ms": time.time() * 1000
            }
            
        except BranchProtectionError as e:
            # Law 4: Fail immediately on immutable repo constraints
            raise OnboardingError(f"Branch protection blocks auto-onboarding: {e}") from e
            
        except CIConfigError as e:
            # Transient CI failure - apply fallback chain
            if attempt == max_retries:
                return {
                    "success": False,
                    "fallback_triggered": True,
                    "fallback_action": "manual_review_template_generated",
                    "reason": str(e),
                    "attempts": attempt + 1
                }
                
    raise OnboardingError(f"Failed to onboard PR after {max_retries + 1} attempts")

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Related Skills

| Skill | Purpose | |

---

---

Constraints

MUST DO

• Validate branch naming conventions and PR scope before creating pull requests — enforce repository-level policies
• Require all CI checks to pass before merging; never allow bypass of required status checks without codeowner approval
• Implement automated changelog generation from commit messages using conventional commits format
• Maintain linear history via rebase on main branch; avoid merge commits except for release branches

MUST NOT DO

• Do not force-push to shared or protected branches — only the original author may force-push their own feature branch
• Avoid squashing all commits during PR review when historical commit context is valuable for understanding evolution
• Never skip required code reviews regardless of how small the change appears — automation cannot assess architectural impact
• Do not create PRs larger than 400 lines of net changes without explicit approval from a senior reviewer

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Onboarding Guide for Open Source (Open Source Guides)
• GitHub CODEOWNERS Documentation
• GitHub Issue Templates
• Contribution Guidelines Template (CONTRIBUTING.md)
• First-Timers Only Help